RoSA: Parameter-Efficient Fine-Tuning for LLMs with RoPE-Aware Selective Adaptation
Published:Nov 21, 2025 09:55
•1 min read
•ArXiv
Analysis
This research paper introduces RoSA, a novel method for parameter-efficient fine-tuning (PEFT) in Large Language Models (LLMs). RoSA leverages RoPE (Rotary Position Embedding) to selectively adapt parameters, potentially leading to improved efficiency and performance.
Key Takeaways
- •RoSA proposes a new PEFT method specifically designed for LLMs.
- •The method is RoPE-aware, leveraging the properties of Rotary Position Embeddings.
- •The research aims to improve efficiency and performance of LLM fine-tuning.
Reference
“RoSA: Enhancing Parameter-Efficient Fine-Tuning via RoPE-aware Selective Adaptation in Large Language Models”